For businesses based on physical retail operation, success of the business depends in part on the customers who live or work in the vicinity and frequent the operation.
Retail or service operations generally refers to businesses, business branches, franchises or, service providers, that are based around a physical location, such as a store, branch, outlet, office or other entity as defined by the business or operator. Customers visit the location to purchase goods and/or receive services from the business.
Since the business generally depends on people visiting the physical location, the business is often interested in information about the people who live or work in the area around the location. This information may reveal competition, market penetration, and growth potential. The information may also be used to generate metrics for comparing a first location with a second location for measuring productivity. Targeted marketing and customer profiling may also be done with this information.
A ‘market area’, ‘trade area’ or ‘service area’ is the area around a physical location that is of interest when analysing the customers and potential customers for the business. A ‘market area’ may be identified in different ways using one of two approaches, a deterministic approach characterized by a hard line boundary around a location and a probabilistic approach with a probabilistic boundary around a location.
In a deterministic approach, each person who lies within the trade area of a certain location, is considered a consumer of interest to the business, and if not, then those persons are not being targeted. This is a very appealing approach due to its conceptual simplicity and ease of use. Examples of deterministic methods include: user drawn market areas, circle market areas, percentage of customers market areas and Thiessen polygon market areas.
User drawn market areas are the most basic deterministic method for defining a retail environment. The method consists of a knowledgeable person within the company hand drawing what is suspected to be the market area for the location. This approach relies heavily on the experience and expertise of the person defining the area. The major flaw of this approach is that it is highly unconstructed and therefore subjective. The potential for inconsistency and error increases in relation to the number of locations within the business and the variety of their types and market settings.
Circle market areas are created by defining a radius and drawing a circle around the location in which it is suspected that your market area lies. This is believed to be the most popular method for defining market areas due to its simplicity and relatively inexpensive cost.
Generally a Geographic Information System (GIS) software package comes equipped to perform this method of market definition. Defining the radius of the circle can be done in several different manners including ad hoc/rule of thumb or by calculating the distance to the closest desired percent of customers. Generally circle trade areas are good representations of patronage when the retail offers of the given locations are undifferentiated (e.g., convenience oriented), the locations are equally accessible from all directions (e.g., the transport surface is uniform) and competition is not a major factor (is weak or ubiquitous). These conditions are not often met and as a result the circle market areas are not a particularly good reflection of the actual retail environment for most retail businesses.
Thiessen polygons are areas drawn on a map around a set of locations in which every point in the defined polygons is closer (in Euclidean distance terms) to the given location than any other location. This method creates market areas which completely fill the map and do not allow any overlap between regions. When using such a method it is common to use all locations competing in the same market (not just ‘own’ locations). The Thiessen approach provides reasonable market area definition under the following conditions: 1. People patronize the closest location (exclusively), 2. All locations are of equal size and overall attractiveness, or, alternatively, the size and attractiveness do not make a difference to customers, 3. The distribution of ‘competing’ locations covers the whole area being analyzed and the locations are effectively a ‘spatial monopolist’, 4. Travel is of equal ease in all directions, 5. There are no barriers to travel. These conditions are rarely met within the retail and service environment.
Percentage of customers is a direct approach to defining market areas. Using customer spotting it is possible to draw polygons enclosing any given number or percent of customers away from the store and in turn define a market area. Customer spotting, or in geographic terms geocoding, can be done several ways and flaws in geocoding will be directly translated into the trade area definition. The user must be aware of this to ensure the best method for customer spotting is performed and the present errors are fixed.
A probabilistic approach or ‘fuzzy trade areas’, calculates probabilities or weights that suggest how likely a consumer is to interact with a given location. There is no clear market boundary for each location and it is up to the user to define at what probability the primary and secondary markets would occur. Furthermore, one consumer can be assigned a probability of interaction with several different locations belonging to the same business. There is no clear edge to a location's market area but rather the probability of interaction decreases, typically with distance. Examples of probabilistic methods include: probabilistic demographics, approximative empirical approaches and statistically modeled approaches.
Probabilistic demographics use weights based on market penetration to define location membership. This approach gives more weight to demographic areas that have larger customer counts. Weights can be calculated several ways including location customers divided by total customers or total customers divided by total population for any given area. Once the weights for each demographic area are calculated, they are multiplied by the variables of interest to produce a more accurate look to the location customer membership.
An approximative empirical approach looks at the distance between customers and location. Such an approach would be used in the instance when customer data is present.
The customers would be plotted and distances would be calculated from the location to the closest 60 and 80 percent or portion of customers. A convex hull could also be created to enclose these primary and secondary market areas. This approach is solely based on proportions of customers. Therefore it is not possible to find areas of higher or lower interaction. However, this approach can be taken a step further and provide information on penetration rates using household or population counts. Taking this extra step allows for picking areas of high or low interaction.
The most frequently utilized statistical approach is the Huff model. The Huff model is a spatial market interaction model (MIM). Such models provide a very useful way to evaluate potential retail sites and forecast potential sales as well as assess the impact of new commercial innovations as they can virtually model entire spatial supply and demand systems. MIMs integrate origin zones, which are places of demand (census tracts or enumeration/dissemination areas) and destinations, which are places of supply (locations, such as stores or branches) through measures of attractiveness, distance, and probability. The measures of attractiveness and distance are subject to a weighting which is manually entered by the user. This weighting process involves subjectivity because the user can continually run the model using different weights until a desired outcome is reached.
As a result, there exists a need for a system and method of analyzing customer information and defining market areas to determine and use market areas for retail operations. Additionally, such a system should also objectively address the issue of customer overlap to multiple retail locations.